9Ecosystems and the Carbon Cycle

Research on the biosphere aims to understand and predict how terrestrial and marine ecosystems are changing, how they are affected by human activity or through their own intrinsic biological dynamics, how they respond to climate variations, and in turn how they affect climate. One of the primary goals of ecosystem research is to determine the amount of primary production, which is most commonly expressed in units of carbon incorporated during photosynthesis and estimates the amount of energy available for higher trophic levels. Since the discovery of the importance of carbon dioxide as a greenhouse gas, the estimation of global carbon fixed by photosynthetic processes has become a central quest in global carbon cycle research and an integral part of climate models.

Before the satellite era, few scientists had attempted to estimate these parameters at a global scale. Instead, most research efforts were dedicated to understanding local dynamics because ecosystem processes are highly variable in response to localized environmental changes. Orbiting satellites provide an ideal vantage point for viewing dynamic ecosystems on the land and in the ocean (Box 9.1). This chapter discusses how the remarkable technological advances of the past decades have enabled scientists to compose routinely global maps of terrestrial and marine productivity, assess the role of the ocean in the global carbon cycle, observe long-term ecosystem trends and atmosphere-biosphere coupling, and even study plant physiology from space.

For the first time, remote sensing made direct global observations of photosynthesis, plant growth, and ecosystem phenology possible, leading to the evolution of a global perspective on ecology (Boxes 9.2 and 9.3). Charles Keeling’s continuous measurements of atmospheric carbon dioxide (CO2) concentrations at Mauna Loa, beginning in 1957, showed a seasonal signal in the atmospheric CO2 concentration due to the terrestrial biosphere being a source and sink for carbon during the winter and summer, respectively. Subsequent work showed that atmospheric CO2 was steadily increasing (Keeling et al. 1976) and that it stemmed from fossil fuel burning, catalyzing an interest in obtaining a global perspective of the carbon cycle. In 1982, the National Aeronautics and Space Administration (NASA) held a workshop in Woods Hole, Massachusetts, on global change (Goody 1982) that spurred a subsequent paper by Tilford (1984) presenting the scientific rationale for the Earth Observing System (EOS). These papers called attention to how anthropogenic global changes might impact ecosystems.

TERRESTRIAL PRIMARY PRODUCTIVITY

New awareness of the relationship between microclimate and plant functions in the 1970s and 1980s spurred the development and evolution of field-portable instruments to measure plant physiological processes, photosynthesis, and transpiration, moving these measurements from the laboratory to the field. Despite these newly available field instruments, global observations of ecosystem and larger-scale processes did not become available until the advent of satellite observations because the field measurements were generally restricted to short-term (seconds to minutes) leaf measurements. The capability of assessing plant productivity from satellite radiance measurements (Box 9.2) opened an entirely new front in ecosystem research. Because small-scale point measurements did not lend themselves well to interpolating and creating global maps, synoptic satellite data provided the first direct globally distributed measurements of terrestrial functioning.

The NASA EOS program brought new capabilities for monitoring terrestrial productivity, with near-daily global coverage of a more capable well-calibrated Moderate Resolution Imaging Spectroradiometer (MODIS) that has allowed development of new biophysical measurements with less reliance on simple empirical indices. One of the new products is the direct global measurement at 1-km resolution of leaf area index (LAI)—an important structural property of

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9
Ecosystems and the Carbon Cycle
Research on the biosphere aims to understand and pre- increasing (Keeling et al. 1976) and that it stemmed from fos-
dict how terrestrial and marine ecosystems are changing, sil fuel burning, catalyzing an interest in obtaining a global
how they are affected by human activity or through their perspective of the carbon cycle. In 1982, the National Aero-
own intrinsic biological dynamics, how they respond to nautics and Space Administration (NASA) held a workshop
climate variations, and in turn how they affect climate. One in Woods Hole, Massachusetts, on global change (Goody
of the primary goals of ecosystem research is to determine 1982) that spurred a subsequent paper by Tilford (1984)
the amount of primary production, which is most commonly presenting the scientific rationale for the Earth Observing
expressed in units of carbon incorporated during photo- System (EOS). These papers called attention to how anthro-
synthesis and estimates the amount of energy available for pogenic global changes might impact ecosystems.
higher trophic levels. Since the discovery of the importance
of carbon dioxide as a greenhouse gas, the estimation of
TERRESTRIAL PRIMARY PRODUCTIVITY
global carbon fixed by photosynthetic processes has become
a central quest in global carbon cycle research and an integral New awareness of the relationship between microcli-
part of climate models. mate and plant functions in the 1970s and 1980s spurred
Before the satellite era, few scientists had attempted to the development and evolution of field-portable instruments
estimate these parameters at a global scale. Instead, most to measure plant physiological processes, photosynthesis,
research efforts were dedicated to understanding local and transpiration, moving these measurements from the
dynamics because ecosystem processes are highly variable in laboratory to the field. Despite these newly available field
response to localized environmental changes. Orbiting satel- instruments, global observations of ecosystem and larger-
lites provide an ideal vantage point for viewing dynamic eco- scale processes did not become available until the advent of
systems on the land and in the ocean (Box 9.1). This chapter satellite observations because the field measurements were
discusses how the remarkable technological advances of the generally restricted to short-term (seconds to minutes) leaf
past decades have enabled scientists to compose routinely measurements. The capability of assessing plant productivity
global maps of terrestrial and marine productivity, assess the from satellite radiance measurements (Box 9.2) opened an
role of the ocean in the global carbon cycle, observe long- entirely new front in ecosystem research. Because small-
term ecosystem trends and atmosphere-biosphere coupling, scale point measurements did not lend themselves well to
and even study plant physiology from space. interpolating and creating global maps, synoptic satellite data
For the first time, remote sensing made direct global provided the first direct globally distributed measurements
observations of photosynthesis, plant growth, and ecosystem of terrestrial functioning.
phenology possible, leading to the evolution of a global per- The NASA EOS program brought new capabilities for
spective on ecology (Boxes 9.2 and 9.3). Charles Keeling’s monitoring terrestrial productivity, with near-daily global
continuous measurements of atmospheric carbon dioxide coverage of a more capable well-calibrated Moderate Reso-
(CO2) concentrations at Mauna Loa, beginning in 1957, lution Imaging Spectroradiometer (MODIS) that has allowed
showed a seasonal signal in the atmospheric CO2 concen- development of new biophysical measurements with less
tration due to the terrestrial biosphere being a source and reliance on simple empirical indices. One of the new prod-
sink for carbon during the winter and summer, respectively. ucts is the direct global measurement at 1-km resolution of
Subsequent work showed that atmospheric CO2 was steadily leaf area index (LAI)—an important structural property of

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EARTH OBSERVATIONS FROM SPACE: THE FIRST 50 YEARS OF SCIENTIFIC ACHIEVEMENTS
BOX 9.1
Ecosystems as Seen from Space
Satellite-based studies of the land and ocean ecosystems rely primarily on imaging sensors measuring radiance in
the visible and near infrared. These spectral bands were ideally suited to monitor plant biomass and primary produc-
tion because the chlorophyll a pigment, found in all marine and terrestrial photosynthetic plants, reflects green light
while absorbing in the blue and red spectral regions. Because plant leaves contain no molecules with high absorption
in the near infrared, they are highly reflective in this region. Therefore, the “greenness” of terrestrial ecosystems can
be mapped by employing the ratio of red to infrared bands. However, this ratio does not work for the ocean because
water is such a strong absorber in the red and infrared that little or no radiation is reflected out of the ocean at those
wavelengths. Instead, the ratio of blue to green bands, after correcting for the atmosphere, has been used to quantify
the chlorophyll concentration in the ocean (Box 9.3).
Remote sensing techniques for mapping and studying terrestrial and marine ecosystems have evolved along differ-
ent paths because of different technological requirements. Compared to the ocean, the land is a bright surface whose
features have distinct spectral signatures and generally sharp boundaries. The spatial scale of such features is on the
order of tens of meters, thus requiring high spatial resolution, but the features generally change slowly over seasons or
longer. In contrast, the ocean is a dark surface with subtle spectral variation that requires high radiometric sensitivity.
Reflectance from the atmosphere dominates the signal received by a satellite over the ocean, and this signal must be
estimated and removed before the ocean signal can be analyzed. Features in the ocean have spatial scales on the
order of tens of kilometers, with fluid boundaries that change on timescales of hours to days. These differences have
led to different sensor and mission requirements, but the goals remain similar. Both terrestrial and marine studies have
sought to quantify primary productivity and the role of the biosphere in the global carbon cycle.
BOX 9.2
Converting Radiance to Plant Productivity
Jordan (1969) was the first to use a ratio of near-infrared and red radiation to estimate biomass and leaf area index
(leaf area/ground surface area) in a forest understory. This study was quickly followed by application of near-infrared/
red ratios to estimate biomass in rangelands (e.g., Pearson and Miller 1972; Rouse et al. 1973, 1974; Maxwell 1976)
and was extended by Carneggie et al. (1974) to the Earth Resources Technology Satellite (ERTS-1) observations of
seasonal growth, which showed that the seasonal peak in the near-infrared/red ratio coincided with maximum foliage
production, thus effectively tracking the phenological cycle.
Rouse et al. (1974) introduced a spectral index, a normalized ratio that reduced illumination differences and other
extrinsic effects by dividing the difference of the two bands by their sum, a ratio adopted as the normalized difference
vegetation index (NDVI). A landmark paper by Tucker (1979) established linear relationships between vegetation spectral
indices (ratios of visible and near-infrared bands) to leaf area and biomass. Following this paper, vegetation indices
rapidly became an established method for analysis of plant biophysical properties using laboratory, field, airborne, and
Landsat data. Today, nearly 2,000 papers have been published using the NDVI, and nearly 6,000 have used some type
of vegetation index to study vegetation. These early studies established that red and near-infrared satellite bands could
track changes in plant growth and development.
the plant canopy used to estimate functional process rates of nological patterns among six global terrestrial biome types.
energy and mass exchange, specifically to calculate rates of LAI is defined as the one-sided leaf area per unit of ground
photosynthesis, evapotranspiration, and respiration (Figure area and is produced by R.B. Myneni, Boston University.
9.1). For the first time this measurement provides a con- An algorithm is used to convert red and near-infrared band
sistent observational basis to estimate and monitor global reflectances to global maps of LAI with modifications for the
productivity. Time series of LAI allow comparison of phe- six biome types, taking into account the directional Sun and

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5
ECOSYSTEMS AND THE CARBON CYCLE
a
b
FIGURE 9.1 February (top panel) and August (bottom panel) 2006 global 4-km monthly composites of leaf area index, computed from the
Moderate Resolution Imaging Spectroradiometer (MODIS; Mod15, collection 4). SOURCE: R.B. Myneni, Boston University, http://dieg.
bu.edu.
9-1 a,b
view factors and measurement uncertainties. Prior to today’s response to climate variability and climate change. This new
satellites, this key biophysical variable was painstakingly observational perspective has led ecologists to see ecosystem
evaluated at the scale of small field sites by dropping a pin processes in an integrated temporal and global context.
or line through the canopy and counting the number of leaves
that were contacted. With the development of red and near-
MARINE PRIMARY PRODUCTIVITY
infrared indices such as normalized difference vegetation
index (NDVI) in the 1980s, it became possible to correlate Approximately half of all global primary production
these ground measurements with index values, allowing the occurs in the ocean, almost entirely due to microscopic
extension of direct measurements to larger regions. single-cell algae known as phytoplankton. In the presence
Today, with MODIS, this observation has become more of ample sunlight and nutrients, phytoplankton reproduce
precise by its extension to a biophysical measurement. Sat- rapidly and biomass can double in a day. As the cells grow
ellite monitoring of the dynamics of Earth’s vegetation is and reproduce, carbon dioxide dissolved in the surface ocean
essential to understanding global ecosystem functioning and is converted to organic matter, which is then consumed by

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EARTH OBSERVATIONS FROM SPACE: THE FIRST 50 YEARS OF SCIENTIFIC ACHIEVEMENTS
BOX 9.3
Global Marine Biomass from Ocean Color Remote Sensing
The ability to derive global maps of chlorophyll a concentration (milligrams per cubic meter) in the upper ocean from
ocean color sensors was a groundbreaking achievement for the oceanographic community (Figure 9.2). This biomass
estimate can then be related to primary productivity and the marine carbon cycle. Although clouds prevent ocean color
sensors to see the entire ocean surface on each orbital pass, a global picture of the distribution of photosynthetic plant
biomass emerges from averaging data over several consecutive days or weeks.
The first ocean color sensor was the Coastal Zone Color Scanner (CZCS), an experimental proof-of-concept mission
operating on the Nimbus 7 satellite between 1978 and 1986. The CZCS demonstrated that it is possible to detect subtle
changes in the color of the ocean and relate these to the concentration of chlorophyll a, the light-harvesting pigment
found in all plants. In particular, chlorophyll a concentrations are quantified by empirical algorithms relating spectral band
ratios (blue to green) to the concentration of chlorophyll in the ocean (Clark 1981, Gordon and Morel 1983, O’Reilly et
al. 2000). A major requirement is that the spectral radiance measurements made by the satellite be corrected to remove
the effect of the atmosphere, which comprises more than 90 percent of the top-of-atmosphere signal. This was a major
technological breakthrough after the launch of the CZCS (Gordon et al. 1980). Contrary to its name, the sensor was
better at estimating biomass in the open ocean than in the coastal zone. Phytoplankton and dissolved organic mat-
ter are the primary sources of optical variability in the open ocean (so-called Case 1 waters [Morel and Prieur 1977,
Gordon and Morel 1983, Siegel et al. 2002]), whereas in coastal regions, mixtures of organic and inorganic materials
affect the ocean color. The problem
of differentiating and quantifying in-
dividual constituent concentrations in
the coastal ocean remains a challenge
today.
The ocean color technology
pioneered by the CZCS has since
been improved and incorporated into
modern space instruments. The first
modern global ocean color sensor was
Japan’s Ocean Color and Temperature
Sensor (OCTS) launched in August
1996 aboard the Advanced Earth
Observing Satellite (ADEOS). The
U.S. Sea-Viewing Wide Field-of-View
Sensor (SeaWiFS) followed in August
1997, shortly after the ADEOS expe-
rienced structural damage after only
9 months in orbit. SeaWiFS is owned
by Orbital Sciences Corporation, with
a guarantee from NASA to buy data
for the scientific research community.
Ocean color data continue to be ac-
quired by the Moderate Resolution
Imaging Spectroradiometers (MODIS)
aboard the Terra and Aqua satellites
launched in 1999 and 2002, respec-
tively, and by a number of other ocean
color instruments operated by other
FIGURE 9.2 Map of chlorophyll a concentration (milligrams per cubic meter) in
the upper Atlantic Ocean derived from data obtained by the Sea-viewing Wide countries (Table 9.1).
Field-of-view Sensor (SeaWiFS). SOURCE: SeaWiFS Project, NASA Goddard
Space Flight Center, and GeoEye.
continued

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EARTH OBSERVATIONS FROM SPACE: THE FIRST 50 YEARS OF SCIENTIFIC ACHIEVEMENTS
zooplankton, fish, and other animals in the “food chain.” Color Scanner (CZCS) data and models of the subsurface
Because of its rapid growth and many consumers, phyto- chlorophyll distribution and Photosynthesis-irradiance (P-I)
plankton biomass or chlorophyll concentration varies on relationships defined for 57 biogeochemical provinces.
short timescales, yet the extent of a “patch” of accumulated
biomass is on the order of 10-100 km.
gLOBAL MARINE AND TERRESTRIAL PRIMARY
Satellites have allowed scientists to routinely estimate
PRODUCTION
phytoplankton productivity on an annual basis for the first
time, enabling them to detect a trend in decreasing phyto- Net primary productivity (NPP) is influenced by climate
plankton productivity associated with warming of the surface and biotic controls that interact with each other. Field et
ocean at mid- to low latitudes. Because a phytoplankton al. (1995) predicted global terrestrial NPP on a monthly
bloom and its associated productivity are such large-scale yet time step using the Carnegie-Ames-Stanford Assimilation
short-lived phenomena, there is simply no way to survey large (CASA) model, incorporating a set of ecological principles
enough areas of the ocean to capture their dynamics using and satellite and surface data. Several authors have used
ships to map phytoplankton biomass and productivity. satellite data to estimate global net primary production,
Prior to the introduction of satellite observations, esti- combining both terrestrial and oceanic models. Within a few
mates of oceanic primary production depended on relatively years they used a linked ocean-terrestrial model that com-
few labor-intensive ship-based incubations using the 14C bined an 8-year Advanced Very High Resolution Radiometer
technique that had become the standard method for measur- (AVHRR) record and a 6-year CZCS data record with a
ing primary productivity in the ocean (Steeman-Nielsen and biogeochemistry model to estimate global land and ocean
Jensen 1957). To estimate global annual oceanic production NPP (Field et al. 1998, Figure 9.3). This study found that
(gigatons of carbon per year), the mean integral productivity the contribution of land and ocean to NPP was nearly equal
was first estimated for the different oceans and depth ranges but that there was striking variability in NPP at a local level.
using relatively few measurements made in each domain. Based on the spatial variability in the satellite data, their
These were then multiplied by the area of the ocean domain model predicted strong differential resource limitations for
and 365 days per year to derive annual oceanic primary pro- terrestrial and ocean habitats.
duction. Due to the vastness of the ocean and high spatial Behrenfeld et al. (2001) used the Sea-Viewing Wide
and temporal variability, ship-based global mapping was Field-of-view Sensor (SeaWiFS) data to estimate terrestrial
infrequently attempted and could not realistically capture and ocean primary production during the transition between
the interannual variability. Even with the development of El Niño and La Niña conditions in 1997 to 1999. They found
fluorescence-based estimates of marine primary productiv- that the ocean exhibited the greatest effect, particularly in
ity, which could be obtained from instruments towed behind tropical regions where El Niño-Southern Oscillation (ENSO)
ships, obtaining global coverage would still require years. It impacts on upwelling and nutrient availability were great-
has long been recognized that ship-based sampling methods est. Terrestrial ecosystems did not exhibit a clear ENSO
suffer from significant undersampling in both space and time response, although regional changes were substantial. These
(McCarthy 1999). Consequently, the best quantitative global studies clearly demonstrate the invaluable contribution satel-
estimates of both biomass and productivity are derived with lite observation of NPP make to the fundamental understand-
the use of satellite observations that provide the necessary ing of climate change impacts on the biosphere.
frequency of global coverage.
To estimate primary productivity from satellite measure-
THE OCEAN CARBON CYCLE
ments, it is assumed that the productivity is proportional to
the phytoplankton biomass. Consequently, measuring bio- Satellite observations afford the only means of estimat-
mass is the first critical step in estimating marine primary ing and monitoring the role of ocean biomass as a sink for
productivity from space. Chlorophyll a, the ubiquitous carbon. In particular, the fundamental question of whether the
light-harvesting pigment found in all green plants, has long biological carbon uptake is changing in response to climate
been a standard measure of phytoplankton biomass (Box 9.3, change can only be addressed with satellite measurements. It
Figure 9.2, Table 9.1). This is largely because chlorophyll requires not only ocean color measurements (phytoplankton
can be measured rapidly and easily owing to its fluorescent biomass and productivity) but also coincident space-based
and absorption properties. observations of the physical ocean environment (circulation
Early estimates of oceanic primary productivity derived and mixing) and land-ocean exchanges through rivers and
using satellite data provided a relative static picture in that tidal wetlands, as well as winds, tides, and solar energy input
they represented average annual productivity (Platt and to the upper ocean. Observing linkages between the physical
Sathyendranath 1988, Antoine et al. 1996, Behrenfeld and and chemical environment and the biology of the ocean is a
Falkowski 1997, Field et al. 1998). One of the most thorough significant achievement of observations from space. Continu-
estimates was that of Longhurst el al. (1995), who estimated ity of this record is critical. Understanding the consequences
global ocean net primary production using Coastal Zone of the CO2 increase and its effect on terrestrial and marine

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ECOSYSTEMS AND THE CARBON CYCLE
LONg-TERM ECOSYSTEM RECORD REVEALS
ecosystems will require global-scale long-term observations
ATMOSPHERE-BIOSPHERE COUPLINg
from carefully calibrated satelliteborne sensors.
Early carbon cycle models that were used to investi-
Although early studies established that red and near-
gate sources and sinks of anthropogenic CO2 ignored the
infrared satellite bands could track changes in plant growth
effects of marine productivity, which was thought to be in
and development (Box 9.1), the large number of Landsat
equilibrium on annual timescales. Since marine productivity
images (~5,000) required to assemble a global database,
is not limited by carbon, it was reasoned that increases in
combined with computational requirements and frequent
CO2 would not affect oceanic productivity. More recently,
cloud cover, have prevented analysis of complete global
modelers have investigated how marine productivity might
or time series of Landsat data sets. Launched in 1978, the
be affected indirectly by climate change through its effect on
Coastal Zone Color Scanner showed that ocean productivity
oceanic and atmospheric circulation patterns.
could be observed using visible and near-infrared bands;
Because phytoplankton life cycles are orders of mag-
however, CZCS measurements were saturated over land and
nitude shorter (days versus years or decades) than those
thus unusable.
of terrestrial plants, phytoplankton may respond to climate
The Advanced Very High Resolution Radiometer on
influences on ocean circulation, mixing, and the supply
the National Oceanic and Atmospheric Administration’s
of nutrients and light much more quickly than plants in
(NOAA) polar-orbiting weather satellites has obtained a
terrestrial ecosystems. Given that oceanic primary produc-
continuous record of daily global observations since 1978,
tivity is estimated to be roughly half of all global primary
acquiring both red and near-infrared bands. Because AVHRR
productivity, the oceanic component of the carbon cycle will
was not designed for observing the terrestrial biosphere and
respond more quickly to climate changes.
the 1- to 8-km scale of AVHRR pixels was significantly
For example, there are vast areas of the Pacific and
larger than theoretical understanding of ecosystem processes,
Southern Oceans, where phytoplankton productivity might
scientists were initially skeptical about whether biospheric
be limited by iron (Martin et al. 1994). In contrast to the
patterns and trends could be observed. However, scientists
other limiting nutrients, which are supplied primarily by the
have managed to overcome technical problems such as
deep ocean, atmospheric dust deposition is one of the main
maintaining calibrations, screening clouds, and adjusting
sources of iron to the open ocean. Paleorecords indicate that
for different observational angles. Thanks to the pioneering
the Southern Ocean responded with increased productivity
efforts of Compton Tucker, the daily AVHRR data set now
during colder periods when iron atmospheric deposition was
spans more than 25 years and is the longest continuous global
enhanced due to the expansion of arid regions. This led to the
record available of terrestrial productivity, phenology, and
notion that these areas in the Pacific and Southern Oceans
ecosystem change for monitoring biospheric responses to
could be stimulated to draw down large amounts of atmo-
climate change and variability. Although AVHRR was not
spheric CO2 if they were provided with iron. Several experi-
designed for climate monitoring, continuing improvements
ments conducted in the late 1990s and early 2000s proved
in calibration and reanalysis have produced a consistent
conclusively that iron does limit production in these regions
record for monitoring and assessing past and future bio-
(Coale et al. 2004). Iron is supplied to the open ocean by
spheric responses resulting from climate change and vari-
atmospheric transport (dust deposition), by lateral advection
ability and anthropogenic activities.
of waters from the continental margins, and by upwelling of
Initial studies using AVHRR followed seasonal and
deep iron-rich waters. Long-term monitoring of the ocean
annual trends in ecosystem production and vegetation phe-
phytoplankton will reveal whether climate change will affect
nology at regional and continental scales (Tucker et al. 1985,
these iron supplies potentially fertilizing the Southern Ocean
Townshend et al. 1985) and at the global scale (Justice et al.
or the Pacific.
1985). In the early 1990s some key papers introduced the use
With 10 years of continuous ocean color data (since
of remote sensing data to ecology (Roughgarden et al. 1991,
1997), we now have the ability to observe year-to-year vari-
Ustin et al. 1991) and stressed the need for ecologists to focus
ability in global oceanic primary production and begin to
on global ecological problems (Mooney 1991). These ideas
assess longer-term trends in ocean carbon uptake. Behrenfeld
led to the resurgence in ecosystem research and modeling
et al. (2006) describe a steady climate-driven decrease in
of biogeochemical processes and significant advances in
oceanic NPP related to the warming of permanently stratified
understanding the Earth as a system.
ocean waters at mid- to low latitudes over the past 8 years.
By the mid-1990s, global ecosystem and biogeochemical
This period of decreasing NPP followed the rise in NPP
models used satellite data to establish variable vegetation
between the El Niño and La Niña phases. Satellite observa-
composition and abundance (e.g., Biome BioGeochemical
tions afford the only means of estimating and monitoring the
Cycles [BGC], Running and Hunt 1993; CASA, Potter et
role of the ocean biomass as a sink for carbon.
al. 1993). The concept of resource limitations as the control-
ling mechanism determining NPP was established in the late
1980s (Chapin et al. 1987). This placed a premium on direct

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0 EARTH OBSERVATIONS FROM SPACE: THE FIRST 50 YEARS OF SCIENTIFIC ACHIEVEMENTS
satellite observations of vegetation conditions to provide and Figure 9.5), and monitoring the state of the biosphere
more realistic estimates of NPP. Previous estimates used uni- (Anyamba et al. 2001) and other ecosystem phenomena.
form rates of NPP for each land-cover type and assumed that Long-term records of NDVI have revealed its increase in
NPP is proportional to reflected net shortwave radiation. response to a warming climate during the 1980s and early
The relationship between vegetation indices and the 1990s, but this trend has leveled off most recently (Angert
physiological processes of photosynthesis and absorbed et al. 2005).
photosynthetic radiation (APAR) were formalized in theo-
retical analyses by Piers Sellers (1986). These developments
STUDYINg PLANT PHYSIOLOgY FROM SPACE
led to a seminal paper by Tucker et al. (1986) in which it
was shown that changes in the planetary NDVI (greenness) To estimate actual NPP in the presence of environmen-
were strongly correlated with daily dynamics of terrestrial tal stressors, researchers developed methods to remotely
IPAR (intercepted photosynthetically active radiation) and estimate regulatory plant biochemicals. The first advance
atmospheric CO2 concentrations. There is a strong negative was the development of the “photochemical reflectance
correlation between NDVI and atmospheric CO2 such that index” (PRI) by John Gamon and colleagues (Gamon et al.
NDVI is high when CO2 concentrations are low and low 1992) to better predict radiation use efficiency. This index
when CO2 concentrations are high (Figure 9.4). This tem- has had extensive use for noninvasive studies of leaf photo-
poral pattern in ecosystem photosynthesis and respiration synthesis by plant physiologists, although at the image level
demonstrates the dynamic coupling between the biosphere it appears more related to carotenoid content. The PRI has
and the atmosphere. led to a range of other studies to quantify plant pigments
In the past decade, NDVI data from AVHRR have and develop methods for assessing them. These advances
become a critical component in monitoring climate change follow increasingly specific knowledge of spectroscopy of
(Fung et al. 1987, Sellers et al. 1994, Angert et al. 2005), plant properties and how this information can be retrieved
assessing changing length and timing of the growing season from satellite sensors.
(e.g., Justice et al. 1985, Myneni et al. 1997, 1998; Box 9.4, A radiative transfer model, developed by Jacquemoud
FIGURE 9.4 Weighted NDVI data plotted against time and latitude zone. Note the highly seasonal effects in the northern latitudes, the influ-
ence of deserts in the 20°-30° N latitude zone, the generally constant response in equatorial areas, and the influence of the low proportion of
land area south of 30° S. SOURCE: Reprinted with permission from J.E. Pinzon (SSAI-NASA/GSFC) and C.J. Tucker (NASA/GSFC).

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ECOSYSTEMS AND THE CARBON CYCLE
BOX 9.4
Increasing Growing Season
Myneni et al. (1997) published a groundbreaking paper using daily satellite data over a 9-year period to show in-
creases in the length of the growing season in the boreal region. They used a time series of NDVI, a measure of the
photosynthetic activity of vegetation canopies, derived from the daily AVHRR satellite data, and showed an increase
in length of the growing season in the boreal region (north of 45º) of 12 days (8 days in spring and 4 days in autumn)
from 1981 to 1991. They demonstrated that this extension of the growing season and enhanced amplitude of NDVI
over the summer were likely correlated with warmer spring and autumn temperatures over the region. This result
partially corroborated an estimated 7-day extension of the growing season that was inferred from atmospheric CO2
measurements. Uniquely, their analysis detected significant spatial variation in the distribution of enhanced NDVI, with
western and eastern Canada and southern and central Alaska having large increases in contrast with little change in
other areas, such as central Canada and Siberia. Monitoring the spatially variable increase in biospheric activity over
the circumpolar region was only possible because of the availability of polar-orbiting satellites.
Furthermore, scatterometer data from satellites provide further evidence that the growing season has lengthened in
the Arctic region over the past 20 years. Figure 9.5 shows the progression of the spring 2000 thaw in Alaska. Similar
measurements made since 1988 show that the thaw in the Arctic has been advancing by almost 1 day a year. These
observations could not have been made without satellites since melting occurs rapidly across the Arctic during the
period of melt and the timing varies between years, depending on weather conditions.
February 28 April 22 May 30
thawed
frozen
FIGURE 9.5 Progression of the spring thaw in Alaska during the year 2000 with snow and ice (blue), ice and slush
with bare ground (yellow), and water and bare ground (red). A series of SeaWinds scatterometer measurements on the
QuickScat satellite, which are sensitive to water in frozen and liquid states, were used to make these images. SOURCE:
fig 9-5
Kimball et al. (2006). Reprinted with permission from the American Meteorological Society, the American Geophysical
Union, and the Association of American Geographers, copyright 2006.
and Baret (1990), has rigorously demonstrated the potential nitrogen content (Kokaly and Clark 1999). Many of the more
to retrieve several plant biochemicals from reflectance and recent advances are based on new imaging spectroscopy
transmittance data and is in wide use today. As summarized technology using NASA’s Airborne Visible/Infrared Imaging
by Ustin et al. (2004), the list of plant biochemicals has Spectrometer (AVIRIS), an aircraft instrument operated by
become longer with studies of chlorophyll fluorescence the Jet Propulsion Laboratory since 1987. NASA has flown
(Zarco-Tejeda et al. 2000a, b), canopy water content (Gao one hyperspectral imager in space, the Earth Observing-1
and Goetz 1995, Zarco-Tejeda et al. 2003), and canopy Hyperion, which was launched in 2000 as an engineering test

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EARTH OBSERVATIONS FROM SPACE: THE FIRST 50 YEARS OF SCIENTIFIC ACHIEVEMENTS
bed yet has continued to operate to today. This technology increased signal to noise ratios, with simultaneous higher
has significant promise for continued advances in detecting spatial and spectral resolution, radiometrically stable instru-
biochemical properties of interest but also for using the high ments, accurate geolocation of images due to advances in
dimensionality of the data to improve land-cover and land- satellite pointing control and Global Positioning System
use classifications. Several recent studies have used NASA’s (GPS), and development of atmospheric radiative transfer
AVIRIS and other hyperspectral imagers to map invasive models allowing retrieval of accurate reflectance data.
weeds with high specificity (Figures 9.6 and 9.7; see also Computer advances have allowed more complex analytical
Box 9.5, Williams and Hunt 2002, Underwood et al. 2003, methods to be developed that better match the spatial and
Asner and Vitousek 2005). spectral patterns in the data. The extensive research funded
In a span of slightly more than 25 years, NASA instru- by NASA through the Earth Observing System program and
ments and the research supported by the agency have evolved the scientific advances in understanding our home planet
from primitive correlative studies to physically based accu- over the past two decades represent a major achievement of
rate analyses. Understanding has advanced rapidly with the space program.
the synergistic advent of new sensor capabilities such as
FIGURE 9.6 A map of invasive species in the Hawaiian rainforest, measured using NASA’s AVIRIS data and impacts of invasive species
and plant functional types on biogeochemical cycles. SOURCE: Modified from Asner and Vitousek (2005).

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ECOSYSTEMS AND THE CARBON CYCLE
BOX 9.5
Detecting Invasive Plant Species
All global ecosystems, with the possible exception of Antarctica, are impacted by invasive species that are substan-
tially changing their functional and structural integrity. Invasion of natural ecosystems represents a serious threat to
global biodiversity. Factors attributed to the spread of these species include climate change, land use, land conversion,
resource extraction, and habitat fragmentation, combined with international transport. Substantial economic costs are
associated with these changes, from loss of agricultural production and increased wildfire frequency to loss of recre-
ational potential. Costs in the United States alone are estimated to exceed $120 billion per year (Pimentel et al. 2005).
Recent advances in imaging spectroscopy, a technique to measure a detailed spectrum for all pixels in the image have
allowed mapping of individual species and plant communities based on their spectral characteristics. Underwood et
al. (2003) used this data to map invasive species in native shrublands along the central coast of California at Vanden-
berg Air Force Base. Figure 9.7 shows the distribution of invasive species and native plant communities at 3-m pixel
resolution for part of the base along the Pacific Coast shoreline. This information is being used by land managers to
improve efficiencies in eradication and containment programs. Data of the quality required for mapping individual plant
species must currently be acquired by airborne hyperspectral imagers. NASA’s suborbital sciences program has led
to the development of this cutting-edge technology and has supported the research required to use it effectively, as
shown in the figure.
Jubata invaded chaparral
Intact chaparral
Intact scrub
Iceplant invaded scrub
chaparral
Iceplant invaded chaparral
Blue gum
Masked
Road
Coastline
FIGURE 9.7 Distribution of three invasive species—iceplant, jubata grass, and blue gum—in two native shrub eco-
systems—coastal sage scrub and Burton Mesa chaparral—on the central coast of California. The map was produced
from a mosaic of flightlines acquired from airborne NASA AVIRIS data, a 224-band imaging spectrometer measuring
from the visible through the solar infrared (400-2,500 nm) and measured at a nominal 3-m pixel resolution. SOURCE:
Underwood et al. (2006). Reprinted with kind permission of Springer Science and Business Media, copyright 2006.
9-7